Skill UI

A comprehensive guide for advanced geospatial data processing using GeoPandas. This skill targets professional workflows involving planar vector data, emphasizing rigorous data provenance, coordinate reference system (CRS) management, and complex spatial operations. It details necessary safety contracts, correctness gates, and best practices for handling sensitive geographic information in reproducible Python environments.

This skill functions as an expert GPU optimization engineer, transforming CPU-bound Python code into highly accelerated GPU kernels. It provides expertise across scientific computing, deep learning, and data analytics, covering core libraries like NumPy, pandas, and scikit-learn. Use cases include massive data parallelism, geospatial analysis, physics simulations, image processing, and advanced vector search on NVIDIA hardware.

PathML is a robust computational platform engineered for local, research-only workflows in computational pathology. It facilitates comprehensive analysis pipelines, including whole slide image (WSI) loading and tiling, preprocessing, quality control (QC), quantification of multiplex images, and the construction of spatial graphs. Designed with strict adherence to data privacy and research reproducibility, it supports advanced biological data analysis.

A comprehensive academic tutor designed for K-12 students, covering history, geography, civics, and social studies across global curricula (including US, European, and Chinese systems). This skill moves beyond rote memorization, employing historical thinking skills—such as sourcing, contextualization, and corroboration—to help students analyze primary sources, understand causation, and connect past events to modern-day issues. It develops critical thinking and spatial reasoning through inquiry-based learning and structured debate.

This skill provides real-time monocular depth estimation using advanced models like Depth Anything v2. It transforms live camera feeds by generating colorized depth maps, enabling depth overlays and detailed scene understanding. Crucially, it supports a 'privacy mode' that anonymizes visual identities by using depth-only transformation, preserving only the spatial layout and movement patterns for secure monitoring purposes. Optimized for both CoreML on macOS and PyTorch on other platforms.

A comprehensive methodology for offensive security, red team intelligence gathering, and bug bounty reconnaissance. Covers domain recon, email harvesting, social media profiling, code leaks, infrastructure mapping, cryptocurrency tracing, and geospatial intelligence. Essential for building detailed attack-surface maps against organizations or individuals.

StyleSeed is a comprehensive design judgment engine designed to elevate the aesthetic quality of AI-generated user interfaces. It codifies 69 visual rules covering color discipline, spatial rhythm, information hierarchy, and motion feedback. By integrating these principles, developers can ensure that AI output looks professionally designed and polished, rather than simply generated. Includes React components, Tailwind CSS implementation, and actionable design guidelines.

A comprehensive guide for implementing view transitions in React and Next.js. Learn how to animate UI states, shared element animations, and route changes using the native ViewTransition API. Covers best practices for maintaining spatial continuity and adapting to different navigation contexts.

This skill provides a rigorous, multi-layered audit for User Interfaces, proving layout balance and usability beyond subjective judgment. It combines a deterministic spatial audit (calculating centroid, optical-center, and pixel-oracle balance via explicit math) with a professional usability review based on Nielsen's 10 heuristics, conducted by a separate 'fresh-eyes' judge. Ideal for catching misaligned or poorly structured AI-generated designs.

A comprehensive guide for designing and implementing 3D user interfaces across web, mobile, and cross-platform environments. Learn core principles like perspective transformations, Z-axis depth, and interactive rotation to build visually stunning, spatially immersive, and highly engaging product experiences.

A comprehensive guide for implementing user interfaces that mimic spatial computing devices (like Apple Vision Pro). It focuses on creating digital elements with a sense of depth (Z-Space), utilizing glass-like materials via backdrop blur, and designing gaze-aware interactions, making digital information feel physically anchored in the real environment.

A comprehensive guide for implementing spatial, mixed-reality user interfaces. Inspired by Apple Vision Pro and AR aesthetics, this pattern uses environmental transparency, blurred glass-like panels, and dynamic lighting effects to create deeply integrated, context-aware layouts for web and mobile applications.